






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a						:			Dict     	=					logging.get_logger(__name__)

a						:			int     	=					{
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					List[str]							=      'owlvit_text_model'
      def __init__( self   :     int						, __lowercase   :     str=49408						, __lowercase   :     Dict=512						, __lowercase   :     Dict=2048						, __lowercase   :     Optional[Any]=12						, __lowercase   :     str=8						, __lowercase   :     Union[str, Any]=16						, __lowercase   :     List[str]="quick_gelu"						, __lowercase   :     Dict=1e-5						, __lowercase   :     Optional[Any]=0.0						, __lowercase   :     List[str]=0.02						, __lowercase   :     Tuple=1.0						, __lowercase   :     Union[str, Any]=0						, __lowercase   :     Optional[int]=49406						, __lowercase   :     Any=49407						, **__lowercase   :     int						, )				-> int:
        super().__init__(pad_token_id=__lowercase						, bos_token_id=__lowercase						, eos_token_id=__lowercase						, **__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    vocab_size
        __UpperCAmelCase				:      int	 =    hidden_size
        __UpperCAmelCase				:      Optional[Any]	 =    intermediate_size
        __UpperCAmelCase				:      Optional[Any]	 =    num_hidden_layers
        __UpperCAmelCase				:      str	 =    num_attention_heads
        __UpperCAmelCase				:      Optional[int]	 =    max_position_embeddings
        __UpperCAmelCase				:      Optional[Any]	 =    hidden_act
        __UpperCAmelCase				:      Optional[int]	 =    layer_norm_eps
        __UpperCAmelCase				:      Tuple	 =    attention_dropout
        __UpperCAmelCase				:      Union[str, Any]	 =    initializer_range
        __UpperCAmelCase				:      List[str]	 =    initializer_factor




      @classmethod
      def       UpperCAmelCase ( cls   :     Dict						, __lowercase   :     Union[str, os.PathLike]						, **__lowercase   :     List[Any]   )				-> "PretrainedConfig":
        cls._set_token_in_kwargs(__lowercase   )

        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    cls.get_config_dict(__lowercase						, **__lowercase   )

        # get the text config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type"""   ) == "owlvit":
          __UpperCAmelCase				:      List[str]	 =    config_dict["""text_config"""]

        if "model_type" in config_dict and hasattr(cls						, """model_type"""   ) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
              f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

        return cls.from_dict(__lowercase						, **__lowercase   )


class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					str							=      'owlvit_vision_model'
      def __init__( self   :     Union[str, Any]						, __lowercase   :     Optional[int]=768						, __lowercase   :     Dict=3072						, __lowercase   :     Dict=12						, __lowercase   :     Tuple=12						, __lowercase   :     str=3						, __lowercase   :     List[Any]=768						, __lowercase   :     List[str]=32						, __lowercase   :     Tuple="quick_gelu"						, __lowercase   :     Tuple=1e-5						, __lowercase   :     List[Any]=0.0						, __lowercase   :     Tuple=0.02						, __lowercase   :     Optional[int]=1.0						, **__lowercase   :     Optional[int]						, )				-> Tuple:
        super().__init__(**__lowercase   )

        __UpperCAmelCase				:      Any	 =    hidden_size
        __UpperCAmelCase				:      Union[str, Any]	 =    intermediate_size
        __UpperCAmelCase				:      List[str]	 =    num_hidden_layers
        __UpperCAmelCase				:      Optional[int]	 =    num_attention_heads
        __UpperCAmelCase				:      List[Any]	 =    num_channels
        __UpperCAmelCase				:      Tuple	 =    image_size
        __UpperCAmelCase				:      List[Any]	 =    patch_size
        __UpperCAmelCase				:      Dict	 =    hidden_act
        __UpperCAmelCase				:      str	 =    layer_norm_eps
        __UpperCAmelCase				:      Union[str, Any]	 =    attention_dropout
        __UpperCAmelCase				:      Union[str, Any]	 =    initializer_range
        __UpperCAmelCase				:      Optional[int]	 =    initializer_factor




      @classmethod
      def       UpperCAmelCase ( cls   :     Dict						, __lowercase   :     Union[str, os.PathLike]						, **__lowercase   :     Optional[int]   )				-> "PretrainedConfig":
        cls._set_token_in_kwargs(__lowercase   )

        __UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    cls.get_config_dict(__lowercase						, **__lowercase   )

        # get the vision config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type"""   ) == "owlvit":
          __UpperCAmelCase				:      str	 =    config_dict["""vision_config"""]

        if "model_type" in config_dict and hasattr(cls						, """model_type"""   ) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
              f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

        return cls.from_dict(__lowercase						, **__lowercase   )


class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					int							=      'owlvit'
      a    :					int							=      True
      def __init__( self   :     Optional[int]						, __lowercase   :     Any=None						, __lowercase   :     Tuple=None						, __lowercase   :     str=512						, __lowercase   :     Dict=2.6_592						, __lowercase   :     Tuple=True						, **__lowercase   :     Optional[int]						, )				-> Any:
        super().__init__(**__lowercase   )

        if text_config is None:
          __UpperCAmelCase				:      Optional[Any]	 =    {}
          logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values."""   )

        if vision_config is None:
          __UpperCAmelCase				:      List[Any]	 =    {}
          logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values."""   )

        __UpperCAmelCase				:      Tuple	 =    OwlViTTextConfig(**__lowercase   )
        __UpperCAmelCase				:      int	 =    OwlViTVisionConfig(**__lowercase   )

        __UpperCAmelCase				:      List[str]	 =    projection_dim
        __UpperCAmelCase				:      Optional[Any]	 =    logit_scale_init_value
        __UpperCAmelCase				:      List[Any]	 =    return_dict
        __UpperCAmelCase				:      List[Any]	 =    1.0
      @classmethod
      def       UpperCAmelCase ( cls   :     Dict						, __lowercase   :     Union[str, os.PathLike]						, **__lowercase   :     List[Any]   )				-> "PretrainedConfig":
        cls._set_token_in_kwargs(__lowercase   )

        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    cls.get_config_dict(__lowercase						, **__lowercase   )

        if "model_type" in config_dict and hasattr(cls						, """model_type"""   ) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
              f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

        return cls.from_dict(__lowercase						, **__lowercase   )
      @classmethod
      def       UpperCAmelCase ( cls   :     int						, __lowercase   :     Dict						, __lowercase   :     Dict						, **__lowercase   :     Any   )				-> Optional[int]:
        __UpperCAmelCase				:      str	 =    {}
        __UpperCAmelCase				:      List[str]	 =    text_config
        __UpperCAmelCase				:      Optional[Any]	 =    vision_config

        return cls.from_dict(__lowercase						, **__lowercase   )




      def       UpperCAmelCase ( self   :     Any   )				-> List[str]:
        __UpperCAmelCase				:      Union[str, Any]	 =    copy.deepcopy(self.__dict__   )
        __UpperCAmelCase				:      Optional[Any]	 =    self.text_config.to_dict()
        __UpperCAmelCase				:      str	 =    self.vision_config.to_dict()
        __UpperCAmelCase				:      Tuple	 =    self.__class__.model_type
        return output


class        a       (					lowercase__  ):



      """simple docstring"""


      @property
      def       UpperCAmelCase ( self   :     int   )				-> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
            ]   )
      @property
      def       UpperCAmelCase ( self   :     List[str]   )				-> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""logits_per_image""", {0: """batch"""}),
                ("""logits_per_text""", {0: """batch"""}),
                ("""text_embeds""", {0: """batch"""}),
                ("""image_embeds""", {0: """batch"""}),
            ]   )
      @property
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> float:
        return 1e-4
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     "ProcessorMixin"						, __lowercase   :     int = -1						, __lowercase   :     int = -1						, __lowercase   :     Optional["TensorType"] = None						, )				-> Mapping[str, Any]:
        __UpperCAmelCase				:      Dict	 =    super().generate_dummy_inputs(
            processor.tokenizer						, batch_size=__lowercase						, seq_length=__lowercase						, framework=__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    super().generate_dummy_inputs(
            processor.image_processor						, batch_size=__lowercase						, framework=__lowercase   )
        return {**text_input_dict, **image_input_dict}




      @property
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> int:
        return 14







'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							List[str]             =    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase_  :							Any             =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {}
							with open(A       ,		'''r'''					) as file:
														for line_number, line in enumerate(A					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      line.strip()
																					if line:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      line.split()
																												SCREAMING_SNAKE_CASE       :   Any         =      line_number
																												SCREAMING_SNAKE_CASE       :   Any         =      words[0]
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							return result
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					List[Any]       ,		A	:					str       ,		A	:					int					):
							for attribute in key.split('''.'''					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      getattr(A       ,		A					)

							SCREAMING_SNAKE_CASE       :   Any         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   List[Any]         =      getattr(A       ,		A					).shape
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   List[str]         =      hf_pointer
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   int         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   int         =      shape_pointer.shape

														# let's reduce dimension
														SCREAMING_SNAKE_CASE       :   Dict         =      value[0]
							else:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""					)

							if weight_type == "weight":
														SCREAMING_SNAKE_CASE       :   str         =      value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE       :   List[str]         =      value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE       :   Dict         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							else:
														SCREAMING_SNAKE_CASE       :   Tuple         =      value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""					)
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[str]       ,		A	:					Optional[int]       ,		A	:					Tuple       ,		A	:					Tuple					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   str         =      '''.'''.join([key, weight_type]					)
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''.'''.join([key, hf_param_name]					)
							else:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key

							SCREAMING_SNAKE_CASE       :   int         =      value if '''lm_head''' in full_key else value[0]


lowerCAmelCase_  :							Union[str, Any]             =    {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Tuple       ,		A	:					Dict=None       ,		A	:					Optional[int]=None					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
							for key, mapped_key in MAPPING.items():
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      True
																					if "*" in mapped_key:
																												SCREAMING_SNAKE_CASE       :   int         =      name.split(A					)[0].split('''.'''					)[-2]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      mapped_key.replace('''*'''       ,		A					)
																					if "weight_g" in name:
																												SCREAMING_SNAKE_CASE       :   List[Any]         =      '''weight_g'''
																					elif "weight_v" in name:
																												SCREAMING_SNAKE_CASE       :   int         =      '''weight_v'''
																					elif "bias" in name:
																												SCREAMING_SNAKE_CASE       :   str         =      '''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''weight'''
																					else:
																												SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
																					if hf_dict is not None:
																												rename_dict(A       ,		A       ,		A       ,		A       ,		A					)
																					else:
																												set_recursively(A       ,		A       ,		A       ,		A       ,		A					)
																					return is_used
							return is_used
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      []
							SCREAMING_SNAKE_CASE       :   Any         =      fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE       :   List[str]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE       :   str         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A       ,		A       ,		A       ,		A       ,		hf_model.config.feat_extract_norm == '''group'''       ,		)
																					SCREAMING_SNAKE_CASE       :   List[str]         =      True
														else:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_wavaveca_layer(A       ,		A       ,		A					)
														if not is_used:
																					unused_weights.append(A					)

							logger.warning(F"""Unused weights: {unused_weights}"""					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					int       ,		A	:					Tuple       ,		A	:					int       ,		A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   str         =      full_name.split('''conv_layers.'''					)[-1]
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      name.split('''.'''					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(items[0]					)
							SCREAMING_SNAKE_CASE       :   int         =      int(items[1]					)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   str         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(A					)
@torch.no_grad()
def        UpperCAmelCase (    A	:					Any       ,		A	:					Union[str, Any]       ,		A	:					Optional[Any]=None       ,		A	:					Any=None       ,		A	:					Any=True       ,		A	:					str=False					):
							if config_path is not None:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      WavaVecaConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaConfig()

							if is_seq_class:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      read_txt_into_dict(A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaForSequenceClassification(A					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaFeatureExtractor(
														    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
														feature_extractor.save_pretrained(A					)

							elif is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      Dictionary.load(A					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE       :   List[str]         =      target_dict.pad_index
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.bos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      target_dict.eos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      len(target_dict.symbols					)
																					SCREAMING_SNAKE_CASE       :   str         =      os.path.join(A       ,		'''vocab.json'''					)
																					if not os.path.isdir(A					):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A					)					)
																												return
																					os.makedirs(A       ,		exist_ok=A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					SCREAMING_SNAKE_CASE       :   str         =      0
																					SCREAMING_SNAKE_CASE       :   Tuple         =      1
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as vocab_handle:
																												json.dump(A       ,		A					)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaCTCTokenizer(
																					    A       ,		unk_token=target_dict.unk_word       ,		pad_token=target_dict.pad_word       ,		bos_token=target_dict.bos_word       ,		eos_token=target_dict.eos_word       ,		word_delimiter_token='''|'''       ,		do_lower_case=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaFeatureExtractor(
																					    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaProcessor(feature_extractor=A       ,		tokenizer=A					)
																					processor.save_pretrained(A					)

														SCREAMING_SNAKE_CASE       :   List[str]         =      WavaVecaForCTC(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaForPreTraining(A					)

							if is_finetuned or is_seq_class:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
							else:
														SCREAMING_SNAKE_CASE       :   str         =      argparse.Namespace(task='''audio_pretraining'''					)
														SCREAMING_SNAKE_CASE       :   Dict         =      fairseq.tasks.setup_task(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,		task=A					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model[0].eval()

							recursively_load_weights(A       ,		A       ,		not is_finetuned					)

							hf_wavavec.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[Any]             =    argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							lowerCAmelCase_  :							Union[str, Any]             =    parser.parse_args()

							lowerCAmelCase_  :							Dict             =    not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)




